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Paper Citation Record · LEDGER

Tight Generalization Bound for AdaBoost

As of 16 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.26838.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.26838 v1

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measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T19:35:40.341295Z

measured 50 of 50 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

50 of 50 outbound references displayed

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Outbound references

Observation 77af5445-ebe7-4999-90c8-136ba32fa6ec · outbound

This paper cites The Uniform Hardcore Lemma via Approximate Bregman Projections.

Tight Generalization Bound for AdaBoost The Uniform Hardcore Lemma via Approximate Bregman Projections

Reference 1

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Observation 42bd7a06-abe1-4772-a20e-b3238d411337 · outbound

This paper cites Boosting the margin: a new explanation for the effectiveness of voting methods.

Tight Generalization Bound for AdaBoost Boosting the margin: a new explanation for the effectiveness of voting methods

Reference 2

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source=pdf_text observed=2026-07-30T19:35:40.184131Z digest=sha256:c5744468ebc60a85b4c10a8bd0dcaf2dd4211bfcd6ce1c180aa286b91f996097

Observation 224c682c-67cc-497a-b813-fd43c28e86c0 · outbound

This paper cites Agnostic Boosting.

Tight Generalization Bound for AdaBoost Agnostic Boosting

Reference 3

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source=pdf_text observed=2026-07-30T19:35:40.187775Z digest=sha256:8a90cf77cd7dce63722494d1d769173477e883b896779eaf00a2a2d8e6c87961

Observation be7ac5b6-e74e-47ce-94fe-5acf267e2d3e · outbound

This paper cites Learnability and the Vapnik-Chervonenkis dimension.

Tight Generalization Bound for AdaBoost Learnability and the Vapnik-Chervonenkis dimension

Reference 4

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Observation 5e1c8029-c98e-433f-b743-088e12ebd83f · outbound

This paper cites Prediction Games and Arcing Algorithms.

Tight Generalization Bound for AdaBoost Prediction Games and Arcing Algorithms

Reference 5

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Observation 7b609cba-fc4c-43de-b64f-69df69bf8ac1 · outbound

This paper cites Of Dice and Games: A Theory of Generalized Boosting.

Tight Generalization Bound for AdaBoost Of Dice and Games: A Theory of Generalized Boosting

Reference 6

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Observation 3fc8d322-21cf-4109-8556-a3055a2cb0ee · outbound

This paper cites Online Agnostic Boosting via Regret Minimization.

Tight Generalization Bound for AdaBoost Online Agnostic Boosting via Regret Minimization

Reference 7

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Observation 33f711c3-c2ec-4f44-a1cc-d83b6ee5a8e6 · outbound

This paper cites Multiclass Boosting: Simple and Intuitive Weak Learning Criteria.

Tight Generalization Bound for AdaBoost Multiclass Boosting: Simple and Intuitive Weak Learning Criteria

Reference 8

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Observation 00a03b2c-e182-4097-86c4-3845dea4aa67 · outbound

This paper cites Boosting With the L2 Loss.

Tight Generalization Bound for AdaBoost Boosting With the L2 Loss

Reference 9

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Observation fd4b836d-9b21-4b7f-bb47-b626382ece56 · outbound

This paper cites Efficient, Noise-Tolerant, and Private Learning via Boosting.

Tight Generalization Bound for AdaBoost Efficient, Noise-Tolerant, and Private Learning via Boosting

Reference 10

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Observation ad9ffb72-19c3-47da-895c-f5ef166e925f · outbound

This paper cites XGBoost: A Scalable Tree Boosting System.

Tight Generalization Bound for AdaBoost XGBoost: A Scalable Tree Boosting System

Reference 11

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Observation 2e818575-3a42-428c-b383-7be47ba78e51 · outbound

This paper cites 2026 (cit.

Tight Generalization Bound for AdaBoost 2026 (cit

Reference 12

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Observation 16847b63-9e14-4970-8f54-a07d388bfb06 · outbound

This paper cites Boosting, Voting Classifiers and Randomized Sample Compression Schemes.

Tight Generalization Bound for AdaBoost Boosting, Voting Classifiers and Randomized Sample Compression Schemes

Reference 13

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Observation 0f324627-a0e7-4e79-8ebc-505fae6b6be6 · outbound

This paper cites Optimal Parallelization of Boosting.

Tight Generalization Bound for AdaBoost Optimal Parallelization of Boosting

Reference 14

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Observation 6aaab1d8-a3da-404d-a0c5-00d8015a6f9f · outbound

This paper cites Revisiting Agnostic Boosting.

Tight Generalization Bound for AdaBoost Revisiting Agnostic Boosting

Reference 15

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Observation c1800052-c227-4d91-a27d-8d1d9320cc00 · outbound

This paper cites Improving Regressors Using Boosting Techniques.

Tight Generalization Bound for AdaBoost Improving Regressors Using Boosting Techniques

Reference 16

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Observation 1683b7ab-b45c-4482-b133-e07bfa506ba8 · outbound

This paper cites Boosting Methods for Regression.

Tight Generalization Bound for AdaBoost Boosting Methods for Regression

Reference 17

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Observation 0e9fe7ef-0bea-4580-9b62-2d309f76aa66 · outbound

This paper cites TabArena: A Living Benchmark for Machine Learning on Tabular Data.

Tight Generalization Bound for AdaBoost TabArena: A Living Benchmark for Machine Learning on Tabular Data

Reference 18

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Observation 4bb0fb17-480d-477b-9897-880a1db7184e · outbound

This paper cites Distribution-Specific Agnostic Boosting.

Tight Generalization Bound for AdaBoost Distribution-Specific Agnostic Boosting

Reference 19

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Observation 77b63590-83a5-4982-9001-cb881ceaefb8 · outbound

This paper cites A decision-theoretic generalization of on-line learning and an application to boosting.

Tight Generalization Bound for AdaBoost A decision-theoretic generalization of on-line learning and an application to boosting

Reference 20

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Observation e0ce2a9a-ce01-4fc8-908b-9df03f1cc576 · outbound

This paper cites Greedy function approximation: A gradient boosting machine.

Tight Generalization Bound for AdaBoost Greedy function approximation: A gradient boosting machine

Reference 21

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Observation 3e213f97-286f-4f91-afd7-61ba1f4fff47 · outbound

This paper cites On the doubt about margin explanation of boosting.

Tight Generalization Bound for AdaBoost On the doubt about margin explanation of boosting

Reference 22

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Observation 6741dbee-1bf4-41ec-8a4e-88660fe6acb7 · outbound

This paper cites Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning.

Tight Generalization Bound for AdaBoost Optimally-Smooth Adaptive Boosting and Application to Agnostic Learning

Reference 23

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Observation 7bd98715-24c4-4c68-8682-c3536667db19 · outbound

This paper cites Sample-Efficient Agnostic Boosting.

Tight Generalization Bound for AdaBoost Sample-Efficient Agnostic Boosting

Reference 24

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Observation 854674e9-545e-425c-a9d1-d0a7b73d7d18 · outbound

This paper cites Sample-Optimal Agnostic Boosting with Unlabeled Data.

Tight Generalization Bound for AdaBoost Sample-Optimal Agnostic Boosting with Unlabeled Data

Reference 25

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Observation cf0de315-a5c2-4554-be08-1c3090657d5a · outbound

This paper cites Margins are insufficient for explaining gradient boosting.

Tight Generalization Bound for AdaBoost Margins are insufficient for explaining gradient boosting

Reference 26

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Observation 4ae15c1e-7874-467c-b565-8e07af99e537 · outbound

This paper cites Margin- based generalization lower bounds for boosted classifiers.

Tight Generalization Bound for AdaBoost Margin- based generalization lower bounds for boosted classifiers

Reference 27

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Observation 5af49123-7245-4894-832e-335e35c58928 · outbound

This paper cites AdaBoost is not an Optimal Weak to Strong Learner.

Tight Generalization Bound for AdaBoost AdaBoost is not an Optimal Weak to Strong Learner

Reference 28

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Observation 5a59466b-8e7e-45e6-a635-2132f89d9e8f · outbound

This paper cites Improved Margin Generalization Bounds for Voting Classifiers.

Tight Generalization Bound for AdaBoost Improved Margin Generalization Bounds for Voting Classifiers

Reference 29

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Observation 19bb9269-9091-45d0-8e0c-b7512e70b07d · outbound

This paper cites Reproducibility in learning.

Tight Generalization Bound for AdaBoost Reproducibility in learning

Reference 30

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Observation 77089cd2-a400-47fc-aca9-6a2c59b68445 · outbound

This paper cites Potential-Based Agnostic Boosting.

Tight Generalization Bound for AdaBoost Potential-Based Agnostic Boosting

Reference 31

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Observation 8676039c-3cab-49d5-bde2-b8e69883a620 · outbound

This paper cites The Impossibility of Parallelizing Boosting.

Tight Generalization Bound for AdaBoost The Impossibility of Parallelizing Boosting

Reference 32

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Observation 39d98e62-9576-4214-97fb-30b3cfe3e47a · outbound

This paper cites LightGBM: A Highly Efficient Gradient Boosting Decision Tree.

Tight Generalization Bound for AdaBoost LightGBM: A Highly Efficient Gradient Boosting Decision Tree

Reference 33

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Observation ba43b70e-a670-4b24-8ab2-3231cf7f0ac7 · outbound

This paper cites Cryptographic Limitations on Learning Boolean Formulae and Finite Automata.

Tight Generalization Bound for AdaBoost Cryptographic Limitations on Learning Boolean Formulae and Finite Automata

Reference 34

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Observation 7fdfb6ee-27f3-4def-8e3f-b41923a31e79 · outbound

This paper cites Empirical Margin Distributions and Bounding the Generalization Error of Combined Classifiers.

Tight Generalization Bound for AdaBoost Empirical Margin Distributions and Bounding the Generalization Error of Combined Classifiers

Reference 35

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Observation 8125052b-c0b9-44f0-9b73-265e9eb6265c · outbound

This paper cites Improved Replicable Boosting with Majority-of-Majorities.

Tight Generalization Bound for AdaBoost Improved Replicable Boosting with Majority-of-Majorities

Reference 36

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Observation 8a423788-57c9-41bb-94ff-91d240905833 · outbound

This paper cites 2025 (cit.

Tight Generalization Bound for AdaBoost 2025 (cit

Reference 37

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Observation c33d16a2-6f58-4d03-8411-03f9ee2b72f8 · outbound

This paper cites Algorithms and Hardness Results for Parallel Large Margin Learning.

Tight Generalization Bound for AdaBoost Algorithms and Hardness Results for Parallel Large Margin Learning

Reference 38

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Observation 22652b90-7748-468b-b4ab-3fbf7a76254b · outbound

This paper cites The Cost of Parallelizing Boosting.

Tight Generalization Bound for AdaBoost The Cost of Parallelizing Boosting

Reference 39

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Observation 21861db1-0a10-42c8-88d5-650782932294 · outbound

This paper cites A Theory of Multiclass Boosting.

Tight Generalization Bound for AdaBoost A Theory of Multiclass Boosting

Reference 40

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source=pdf_text observed=2026-07-30T19:35:40.312419Z digest=sha256:fc0d7ea4bd5c10dbcefe7868d5337c99a315a650bf1aec1022f6bacdba62db76

Observation 4e0a86c4-1e90-4608-a32b-51607dc6f42f · outbound

This paper cites CatBoost: unbiased boosting with categorical features.

Tight Generalization Bound for AdaBoost CatBoost: unbiased boosting with categorical features

Reference 41

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source=pdf_text observed=2026-07-30T19:35:40.315682Z digest=sha256:278b2805d8b68526e87c15a3003ea51739f32c48f36c3b3631e3ae7af672513e

Observation 3c2bcb03-7b79-4648-8857-45f449e68ab8 · outbound

This paper cites Chaining.

Tight Generalization Bound for AdaBoost Chaining

Reference 42

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no resolver link, observed 2026-07-30T19:35:40.319155Z

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source=pdf_text observed=2026-07-30T19:35:40.319155Z digest=sha256:1a06e18e994b40b1c108b4614a04a86b84eca3b5bb0d7a5866eebebc720cbc72

Observation d6495462-cba8-4677-8032-cd2c614d3af3 · outbound

This paper cites The Strength of Weak Learnability (Extended Abstract).

Tight Generalization Bound for AdaBoost The Strength of Weak Learnability (Extended Abstract)

Reference 43

Resolution
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no resolver link, observed 2026-07-30T19:35:40.322159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T19:35:40.322159Z digest=sha256:6dc458c7939f53a64276ed16c9c1dd93770f0c96d71a766add51e3305f3886e7

Observation cb50ec24-3b8c-46d6-a595-92c7daeecba5 · outbound

This paper cites Schapire and Yoav Freund.Boosting: Foundations and Algorithms.

Tight Generalization Bound for AdaBoost Schapire and Yoav Freund.Boosting: Foundations and Algorithms

Reference 44

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no resolver link, observed 2026-07-30T19:35:40.325339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T19:35:40.325339Z digest=sha256:6fccaa93cba8fa3eb0049c52267ab9210f005c1fbe26dd10a43aa15d71eeade3

Observation 45d7fc47-6b8e-4a63-b3a7-b7117400c25d · outbound

This paper cites Improved boosting algorithms using confidence-rated pre- dictions.

Tight Generalization Bound for AdaBoost Improved boosting algorithms using confidence-rated pre- dictions

Reference 45

Resolution
unresolved
no resolver link, observed 2026-07-30T19:35:40.328460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T19:35:40.328460Z digest=sha256:48d52f9929bb3ffd88a7cb2563f23210cd1d2725db33278312ca1208bd4bcc28

Observation 6af820f7-ef7c-4c50-bb5d-b22e7830bd6d · outbound

This paper cites Smooth Boosting and Learning with Malicious Noise.

Tight Generalization Bound for AdaBoost Smooth Boosting and Learning with Malicious Noise

Reference 46

Resolution
unresolved
no resolver link, observed 2026-07-30T19:35:40.331628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T19:35:40.331628Z digest=sha256:703997531ad4d948979fe01b7e5933d145f47b9777b72619b01ac133a823ee46

Observation f19a6159-0fc2-4790-b4ce-92768b5bde74 · outbound

This paper cites Optimistic Rates for Learning with a Smooth Loss.

Tight Generalization Bound for AdaBoost Optimistic Rates for Learning with a Smooth Loss

Reference 47

Resolution
unresolved
no resolver link, observed 2026-07-30T19:35:40.334534Z

Source-reported events for the cited work

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source=pdf_text observed=2026-07-30T19:35:40.334534Z digest=sha256:21a01b42927f36f923685fb35593bad40648d543f0e56e5117ca27f955028551

Observation 04a75352-a130-44d1-9b53-720174798a3b · outbound

This paper cites A Theory of the Learnable.

Tight Generalization Bound for AdaBoost A Theory of the Learnable

Reference 48

Resolution
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no resolver link, observed 2026-07-30T19:35:40.337848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T19:35:40.337848Z digest=sha256:72985bf2ac8875143d4ce4e6af67f1645ff708c901a0a4e32441c9bdd88165d8

Observation 8c9214d8-d6e9-42aa-850f-6d47e6df8067 · outbound

This paper cites On the uniform convergence of relative frequencies of events to their probabilities.

Tight Generalization Bound for AdaBoost On the uniform convergence of relative frequencies of events to their probabilities

Reference 49

Resolution
unresolved
no resolver link, observed 2026-07-30T19:35:40.341295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T19:35:40.341295Z digest=sha256:e925a8b6599886d41133f4a4e3e83a34a52fe16dd06e3707125ffd338b99c7c3

Observation b65ed83b-96ab-47d9-861c-469ae3e037a0 · outbound

This paper cites an unresolved cited work.

Tight Generalization Bound for AdaBoost Unresolved cited work

Reference 2009

Resolution
unresolved
no resolver link, observed 2026-07-30T19:35:40.283997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T19:35:40.283997Z digest=sha256:8a5505376da4b4507236b3ec3d26f741b1c221bb545d1a06164ba7a7a29931f7

Pith citing papers

No inbound Pith citation observations are available.